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  • MALE SPEAKER: So today we're here to see Jerry Kaplan.

  • He's co-founded four startups, two of which

  • have gone public-- serial entrepreneur, widely

  • respected as a technical innovator,

  • and a bestselling author.

  • In the interest of time though, I'm

  • going to abridge his long list of many accomplishments

  • and talk about a few things that I think will especially

  • interest you before he talks about things that especially

  • interest him.

  • So one especially interesting thing that he's done-- he

  • was the co-founder, alongside his friends Kevin Doren

  • and Robert Carr, of the GO Corporation in 1987.

  • They were pioneers in the work of tablet and stylus-based

  • computing, the precursors to the Apple Newton, the Palm Pilot,

  • and later smartphones and tablets of today.

  • If you chronicled, actually, his time there,

  • they have a very interesting book called "Startup:

  • The Silicon Valley Adventure."

  • Some of you may have heard of this.

  • A fun little fact.

  • Omid Kordestani-- some of you may know him

  • as our former chief business officer, started

  • at Google in 1999-- got his start, actually, at Go Corp.

  • Jerry may talk about that.

  • I don't know.

  • It's possible.

  • Here he is today again to talk about artificial intelligence

  • and the changing world of work automation.

  • So we are here for "Humans Need Not Apply."

  • Give a warm welcome to Jerry Kaplan, everyone.

  • [APPLAUSE]

  • JERRY KAPLAN: Thanks, how's the mic?

  • Oh, that's good.

  • All right, well, a mentor of mine used to say never

  • give a talk the first time.

  • I want you to know I've put together

  • a special talk for you guys.

  • This is the first I'm giving it.

  • We'll see what happens.

  • I should leave some time.

  • I have lots of weird anecdotes about Google

  • that I will be happy to tell when I'm not on the camera,

  • as long as I'm not being recorded.

  • OK, so now for something completely different, as

  • they used to say on "Monty Python."

  • The common wisdom about artificial intelligence

  • is that we're building increasingly intelligent

  • machines that are ultimately going

  • to surpass human capabilities and steal our jobs

  • and maybe even escape human control

  • and take over the world.

  • So I'm going to present the case today

  • that that narrative is both misguided and

  • counterproductive-- that a more appropriate way to frame

  • this, which is really better supported

  • by actual historical and current events,

  • is that AI is simply a natural extension

  • of long-standing efforts to automate tasks that date back

  • at least to the start of the Industrial Revolution.

  • And then I want to talk about the consequences

  • if you think about it in that particular way.

  • But let me ask about the audience-- how many of you

  • are engineers?

  • OK, how many of you are not engineers?

  • Two.

  • How many people haven't raised their hand yet?

  • Nobody.

  • OK.

  • And that's called closure, right?

  • OK, and how many of you are doing anything

  • even vaguely related to AI?

  • Oh, not that many, OK.

  • Cool.

  • At least you won't admit it by the time I'm done with my talk,

  • I think.

  • OK, so let me start with a little bit of a history lesson.

  • I'm teaching Impact of Artificial Intelligence

  • at Stanford.

  • And much to my shock, the students

  • who studied artificial intelligence

  • don't know much about its history.

  • So here's a kind of irreverent view.

  • I'm going to start with an unorthodox history of AI.

  • Now, here's a news flash for you.

  • Science does not proceed scientifically.

  • So it's like the making of legislation and sausage.

  • Perhaps this is better done outside of the public view.

  • More than you might want to believe,

  • progress is often due to the clash of egos

  • and ideas and institutions.

  • You guys work in an institution.

  • I'm sure you see that occasionally.

  • And artificial intelligence is no exception,

  • so let me start right at the beginning.

  • Dartmouth College, 1956.

  • A group of scientists-- they got together

  • for an extended working session.

  • How many of you who John McCarthy is?

  • Oh, man, OK.

  • He's a mathematician who was then employed at Dartmouth.

  • Now, he hosted this meeting along with-- raise your hand

  • if you know these guys-- Marvin Minsky.

  • Oh, more than John, OK.

  • He was then at Harvard.

  • Claude Shannon?

  • That's good.

  • You guys should know who Shannon is.

  • He was at Bell Laboratories.

  • And Nathaniel Rochester?

  • Probably no hands.

  • One hand.

  • Are you his son?

  • Sorry?

  • AUDIENCE: [INAUDIBLE].

  • JERRY KAPLAN: OK, there you go.

  • He was at IBM.

  • Now, here's what these guys had to say,

  • or John McCarthy had to say.

  • He called his proposal "A Proposal

  • for the Dartmouth Summer Research Project

  • on Artificial Intelligence."

  • Now, this was the first known use of the term artificial

  • intelligence.

  • But what's not commonly known is why did John McCarthy choose

  • that particular name?

  • He explained this later-- much later,

  • actually-- his motivation.

  • He said, "As for myself, one of the reasons

  • for inventing the term artificial intelligence

  • was to escape the association with cybernetics.

  • Its concentration on analog feedback seemed misguided,

  • and I wished to avoid having either

  • to accept Norbert Wiener as a guru

  • or having to argue with him."

  • Now, Norbert Wiener, as you may know,

  • was a highly respected-- Norbert Wiener?

  • Anybody?

  • Oh, my god.

  • OK.

  • Cybernetics.

  • Cybernetics.

  • Good, you've heard the term at least.

  • He was a highly respected senior mathematician

  • and a philosopher at MIT.

  • Now, while he was that, McCarthy, this guy,

  • was just a junior professor at Dartmouth.

  • So he didn't want to have to go up against the powers that be.

  • So to understand the original intention of the founding

  • fathers of AI, it's worth reading

  • some of the actual text of this conference proposal.

  • I think it's on the screen.

  • "The study is to proceed on the basis of the conjecture

  • that every aspect of learning or any other feature

  • of intelligence can in principle be so precisely described

  • that a machine can be made to simulate it.

  • An attempt will be made to find out

  • how to make machines use language,

  • form abstractions and concepts, solve

  • kinds of problems now reserved for humans,

  • and improve themselves."

  • It's 1950-- what was it, 1956?

  • "We think that a significant advance

  • could be made in one or more these problems if a carefully

  • selected group of scientists work on it together

  • for a summer."

  • Now, that's a pretty dubious agenda for a summer break.

  • Now, many of the Dartmouth conference participants

  • had their own view about how to best approach

  • artificial intelligence.

  • But John McCarthy's specialty was mathematical logic.

  • In particular, he believed that logical inference

  • was the key to, using his words, simulated intelligence.

  • That's what he thought AI was.

  • Now, his approach, skipping ahead quite a ways,

  • but his approach eventually became

  • known as what's called the physical symbol systems

  • hypothesis.

  • Anybody here have heard of that?

  • One.

  • Good man, OK, you can take over for the rest of the talk.

  • Now, that was the dominant paradigm

  • in the field of artificial intelligence for the first 30

  • years or so after the Dartmouth Conference.

  • Now, here's John McCarthy.

  • I'm old enough to have known John McCarthy when

  • I was a postdoc at Stanford, where he founded the Stanford

  • Artificial Intelligence Lab.

  • Now, John was definitely a brilliant scientist.

  • He invented the programming language Lisp.

  • Good.

  • And he invented the concept of time sharing.

  • Not too many people know that.

  • But he definitely had the mad professor thing going.

  • Let's see if this works.

  • Almost.

  • I'm using somebody else's computer.

  • You know, he had the wild eyes and the hair.

  • The guy on the right, as you may know,

  • is Professor Emmett Brown, who invented the-- what is it?

  • The flux capacitor time machine.

  • How many people know the flux capacitor?

  • OK, good.

  • I'm just checking to make sure this talk works.

  • But I'm confident that John McCarthy, having met him,

  • never really expected that his clever name emerging

  • field is going to turn out to be one

  • of the great accidental marketing coups of all time.

  • So it's not only inspired generations of researchers,

  • including myself, but it spawned a virtual industry

  • of science fiction and Hollywood blockbusters and media

  • attention and pontificating pundits, also including myself.

  • How do you name the field something less arousing,

  • like logical programming, or symbolic systems?

  • I doubt very many of us would have ever heard

  • of the field today.

  • The field would have just motored

  • along automating various tasks while we marvelled

  • at the cleverness not of what the creations were,

  • but of the engineers.

  • I'm getting a little bit ahead of my story.

  • In any case, McCarthy's hypothesis

  • that logic was the basis of human intelligence is, at best,

  • questionable.

  • Today, in fact, most AI researchers

  • have abandoned this approach and believe

  • it was just plain wrong.

  • The symbolic system approach has been almost entirely abandoned

  • in favor of generally what's now referred

  • to as machine learning.

  • How many people here are doing machine learning?

  • Good.

  • OK, or you certainly know about it.

  • But rejecting that old approach is throwing the baby out

  • with the bathwater.

  • Some truly important advances in computing

  • came out of symbolic systems, including

  • things like heuristic search algorithms, logical problem

  • solvers, game players, reasoning systems.

  • These were all the old approach.

  • And many of the results of all that work

  • are in wide practical use today.

  • For example, formulating driving directions-- I got

  • lost coming here.

  • I didn't know the difference between the express lane

  • and the regular lane.

  • I thought I was in the other one.

  • Take this exit.

  • No exit.

  • Laying out factories and warehouses,

  • proving that complex computer chips actually

  • meet their specifications-- this all uses early AI techniques.

  • And I'm sure that there are many more of these to come.

  • Now, did I mention machine learning?

  • It's certainly the focus of most current research,

  • and in some circles, at least where I am,

  • it's considered a serious candidate

  • for the real basis of human intelligence.

  • Now, my personal opinion is that while it's

  • a very powerful technology, and it's